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Don't Guess, Escalate: Towards Explainable Uncertainty-Calibrated AI Forensic Agents
Authors:
Giulia Boato,
Andrea Montibeller,
Edward Delp,
Luisa Verdoliva,
Daniele Miorandi
Abstract:
AI is reshaping the landscape of multimedia forensics. We propose AI forensic agents: reliable orchestrators that select and combine forensic detectors, identify provenance and context, and provide uncertainty-aware assessments. We highlight pitfalls in current solutions and introduce a unified framework to improve the authenticity verification process.
AI is reshaping the landscape of multimedia forensics. We propose AI forensic agents: reliable orchestrators that select and combine forensic detectors, identify provenance and context, and provide uncertainty-aware assessments. We highlight pitfalls in current solutions and introduce a unified framework to improve the authenticity verification process.
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Submitted 18 December, 2025;
originally announced December 2025.
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Towards Open Diversity-Aware Social Interactions
Authors:
Loizos Michael,
Ivano Bison,
Matteo Busso,
Luca Cernuzzi,
Amalia De Götzen,
Shyam Diwakar,
Kobi Gal,
Amarsanaa Ganbold,
George Gaskell,
Daniel Gatica-Perez,
Jessica Heesen,
Daniele Miorandi,
Salvador Ruiz-Correa,
Laura Schelenz,
Avi Segal,
Carles Sierra,
Hao Xu,
Fausto Giunchiglia
Abstract:
Social Media and the Internet have catalyzed an unprecedented potential for exposure to human diversity in terms of demographics, talents, opinions, knowledge, and the like. However, this potential has not come with new, much-needed, instruments and skills to harness it. This paper presents our work on promoting richer and deeper social relations through the design and development of the "Internet…
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Social Media and the Internet have catalyzed an unprecedented potential for exposure to human diversity in terms of demographics, talents, opinions, knowledge, and the like. However, this potential has not come with new, much-needed, instruments and skills to harness it. This paper presents our work on promoting richer and deeper social relations through the design and development of the "Internet of Us", an online platform that uses diversity-aware Artificial Intelligence to mediate and empower human social interactions. We discuss the multiple facets of diversity in social settings, the multidisciplinary work that is required to reap the benefits of diversity, and the vision for a diversity-aware hybrid human-AI society.
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Submitted 2 January, 2026; v1 submitted 17 February, 2025;
originally announced March 2025.
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Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK
Authors:
Karim Assi,
Lakmal Meegahapola,
William Droz,
Peter Kun,
Amalia de Gotzen,
Miriam Bidoglia,
Sally Stares,
George Gaskell,
Altangerel Chagnaa,
Amarsanaa Ganbold,
Tsolmon Zundui,
Carlo Caprini,
Daniele Miorandi,
Alethia Hume,
Jose Luis Zarza,
Luca Cernuzzi,
Ivano Bison,
Marcelo Dario Rodas Britez,
Matteo Busso,
Ronald Chenu-Abente,
Fausto Giunchiglia,
Daniel Gatica-Perez
Abstract:
Smartphones enable understanding human behavior with activity recognition to support people's daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study se…
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Smartphones enable understanding human behavior with activity recognition to support people's daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous populations within a country. However, people are more sedentary in the post-pandemic world with the prevalence of remote/hybrid work/study settings, making detecting simple activities less meaningful for context-aware applications. Hence, the understanding of (i) how multimodal smartphone sensors and machine learning models could be used to detect complex daily activities that can better inform about people's daily lives and (ii) how models generalize to unseen countries, is limited. We analyzed in-the-wild smartphone data and over 216K self-reports from 637 college students in five countries (Italy, Mongolia, UK, Denmark, Paraguay). Then, we defined a 12-class complex daily activity recognition task and evaluated the performance with different approaches. We found that even though the generic multi-country approach provided an AUROC of 0.70, the country-specific approach performed better with AUROC scores in [0.79-0.89]. We believe that research along the lines of diversity awareness is fundamental for advancing human behavior understanding through smartphones and machine learning, for more real-world utility across countries.
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Submitted 16 February, 2023;
originally announced February 2023.
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Generalization and Personalization of Mobile Sensing-Based Mood Inference Models: An Analysis of College Students in Eight Countries
Authors:
Lakmal Meegahapola,
William Droz,
Peter Kun,
Amalia de Gotzen,
Chaitanya Nutakki,
Shyam Diwakar,
Salvador Ruiz Correa,
Donglei Song,
Hao Xu,
Miriam Bidoglia,
George Gaskell,
Altangerel Chagnaa,
Amarsanaa Ganbold,
Tsolmon Zundui,
Carlo Caprini,
Daniele Miorandi,
Alethia Hume,
Jose Luis Zarza,
Luca Cernuzzi,
Ivano Bison,
Marcelo Rodas Britez,
Matteo Busso,
Ronald Chenu-Abente,
Can Gunel,
Fausto Giunchiglia
, et al. (2 additional authors not shown)
Abstract:
Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies…
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Mood inference with mobile sensing data has been studied in ubicomp literature over the last decade. This inference enables context-aware and personalized user experiences in general mobile apps and valuable feedback and interventions in mobile health apps. However, even though model generalization issues have been highlighted in many studies, the focus has always been on improving the accuracies of models using different sensing modalities and machine learning techniques, with datasets collected in homogeneous populations. In contrast, less attention has been given to studying the performance of mood inference models to assess whether models generalize to new countries. In this study, we collected a mobile sensing dataset with 329K self-reports from 678 participants in eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, UK) to assess the effect of geographical diversity on mood inference models. We define and evaluate country-specific (trained and tested within a country), continent-specific (trained and tested within a continent), country-agnostic (tested on a country not seen on training data), and multi-country (trained and tested with multiple countries) approaches trained on sensor data for two mood inference tasks with population-level (non-personalized) and hybrid (partially personalized) models. We show that partially personalized country-specific models perform the best yielding area under the receiver operating characteristic curve (AUROC) scores of the range 0.78-0.98 for two-class (negative vs. positive valence) and 0.76-0.94 for three-class (negative vs. neutral vs. positive valence) inference. Overall, we uncover generalization issues of mood inference models to new countries and how the geographical similarity of countries might impact mood inference.
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Submitted 5 November, 2022;
originally announced November 2022.
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Blockchain enabled fog structure to provide data security in IoT applications
Authors:
Mozhdeh Farhadi,
Daniele Miorandi,
Guillaume Pierre
Abstract:
IoT provides services by connecting smart devices to the Internet, and exploiting data generated by said devices to enable value-added services to individuals and businesses. In such cases, if data is exposed, tampered or lost, the service would not behave correctly. In this article, we discuss data security in IoT applications across five dimensions: confidentiality, integrity, authenticity, non-…
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IoT provides services by connecting smart devices to the Internet, and exploiting data generated by said devices to enable value-added services to individuals and businesses. In such cases, if data is exposed, tampered or lost, the service would not behave correctly. In this article, we discuss data security in IoT applications across five dimensions: confidentiality, integrity, authenticity, non-repudiation and availability. We discuss how distributed ledger technology could be used to overcome these issues and propose to use a fog computing architecture as decentralized computational support to deploy the ledger.
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Submitted 15 January, 2019;
originally announced January 2019.
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A Wikipedia-based approach to profiling activities on social media
Authors:
Christian Torrero,
Carlo Caprini,
Daniele Miorandi
Abstract:
Online user profiling is a very active research field, catalyzing great interest by both scientists and practitioners. In this paper, in particular, we look at approaches able to mine social media activities of users to create a rich user profile. We look at the case in which the profiling is meant to characterize the user's interests along a set of predefined dimensions (that we refer to as categ…
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Online user profiling is a very active research field, catalyzing great interest by both scientists and practitioners. In this paper, in particular, we look at approaches able to mine social media activities of users to create a rich user profile. We look at the case in which the profiling is meant to characterize the user's interests along a set of predefined dimensions (that we refer to as categories). A conventional way to do so is to use semantic analysis techniques to (i) extract relevant entities from the online conversations of users (ii) mapping said entities to the predefined categories of interest. While entity extraction is a well-understood topic, the mapping part lacks a reference standardized approach. In this paper we propose using graph navigation techniques on the Wikipedia tree to achieve such a mapping. A prototypical implementation is presented and some preliminary results are reported.
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Submitted 26 September, 2018; v1 submitted 6 April, 2018;
originally announced April 2018.
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Emergence of Equilibria from Individual Strategies in Online Content Diffusion
Authors:
Eitan Altman,
Francesco De Pellegrini,
Rachid El-Azouzi,
Daniele Miorandi,
Tania Jimenez
Abstract:
Social scientists have observed that human behavior in society can often be modeled as corresponding to a threshold type policy. A new behavior would propagate by a procedure in which an individual adopts the new behavior if the fraction of his neighbors or friends having adopted the new behavior exceeds some threshold. In this paper we study the question of whether the emergence of threshold poli…
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Social scientists have observed that human behavior in society can often be modeled as corresponding to a threshold type policy. A new behavior would propagate by a procedure in which an individual adopts the new behavior if the fraction of his neighbors or friends having adopted the new behavior exceeds some threshold. In this paper we study the question of whether the emergence of threshold policies may be modeled as a result of some rational process which would describe the behavior of non-cooperative rational members of some social network. We focus on situations in which individuals take the decision whether to access or not some content, based on the number of views that the content has. Our analysis aims at understanding not only the behavior of individuals, but also the way in which information about the quality of a given content can be deduced from view counts when only part of the viewers that access the content are informed about its quality. In this paper we present a game formulation for the behavior of individuals using a meanfield model: the number of individuals is approximated by a continuum of atomless players and for which the Wardrop equilibrium is the solution concept. We derive conditions on the problem's parameters that result indeed in the emergence of threshold equilibria policies. But we also identify some parameters in which other structures are obtained for the equilibrium behavior of individuals.
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Submitted 31 December, 2012;
originally announced December 2012.
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Distributed Wake-Up Scheduling for Energy Saving in Wireless Networks
Authors:
Francesco De Pellegrini,
Karina Gomez,
Daniele Miorandi,
Imrich Chlamtac
Abstract:
A customary solution to reduce the energy consumption of wireless communication devices is to periodically put the radio into low-power sleep mode. A relevant problem is to schedule the wake-up of nodes in such a way as to ensure proper coordination among devices, respecting delay constraints while still saving energy. In this paper, we introduce a simple algebraic characterization of the problem…
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A customary solution to reduce the energy consumption of wireless communication devices is to periodically put the radio into low-power sleep mode. A relevant problem is to schedule the wake-up of nodes in such a way as to ensure proper coordination among devices, respecting delay constraints while still saving energy. In this paper, we introduce a simple algebraic characterization of the problem of periodic wake-up scheduling under both energy consumption and delay constraints. We demonstrate that the general problem of wake-up times coordination is equivalent to integer factorization and discuss the implications on the design of efficient scheduling algorithms. We then propose simple polynomial time heuristic algorithms that can be implemented in a distributed fashion and present a message complexity of the order of the number of links in the network. Numerical results are provided in order to assess the performance of the proposed techniques when applied to wireless sensor networks.
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Submitted 8 June, 2011;
originally announced June 2011.
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Distributed k-Core Decomposition
Authors:
Alberto Montresor,
Francesco De Pellegrini,
Daniele Miorandi
Abstract:
Among the novel metrics used to study the relative importance of nodes in complex networks, k-core decomposition has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-core decomposition of a network, with the purpose of (i) enabli…
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Among the novel metrics used to study the relative importance of nodes in complex networks, k-core decomposition has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-core decomposition of a network, with the purpose of (i) enabling the run-time computation of k-cores in "live" distributed systems and (ii) allowing the decomposition, over a set of connected machines, of very large graphs, that cannot be hosted in a single machine. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world graphs is provided.
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Submitted 29 March, 2011; v1 submitted 28 March, 2011;
originally announced March 2011.